Parameter Identification of a Robot Arm Manipulator Based on a Convolutional Neural Network

León, Carlos Leopoldo Carreón Díaz de; Vergara-Limon, Sergio; Vargas-Treviño, María Aurora Diozcora; Gomez, J L Zamorano; Calleros, Juan Manuel Gonzalez; González-Arriaga, Daniel Marcelo; Vargas-Treviño, Marciano

doi:10.1109/access.2022.3177209

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…= L (3) xy − a (4) l (4) z − 6 i=4 a (4) d ( 4) M (i) , β (11) = L (3) xz , β (12) = L (3) yz , β (13) = L (3) zz + L (4) yy + 2d (4)…”

Section: Appendix a Symbolic Expressions Of The Base Parameters Of Th...mentioning

confidence: 99%

“…However, modeling accuracy of a dynamic model depends on its sensitivity with respect to environmental noise, especially non-Gaussian noise commonly seen in measurements [7]. Many different types of dynamic model identification approaches have been recently proposed, such as least squares (LS)-based methods [8], [9], iterative-based methods [10], [11] and deep learning-based methods [12], [13]. To simplify the dynamic model and also improve the The associate editor coordinating the review of this manuscript and approving it for publication was Ángel F. García-Fernández .…”

Section: Introductionmentioning

confidence: 99%

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A Rapid Base Parameter Physical Feasibility Test Algorithm for Industrial Robot Manipulator Identification Using a Recurrent Neural Network

Huang,

Cheng,

Huang

2023

IEEE Access

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A rapid recurrent neural network (RNN)-based physical feasibility test algorithm for base parameters during the identification process of a robot dynamic model is proposed in this paper. Firstly, related physical constraints such as inertia tensor, drive chain inertia, and friction are combined into the formulation of linear matrix inequality (LMI) to examine the physical consistency of the base parameters. The optimization problem of LMI is then solved by a matrix-oriented gradient-type RNN. Since the network structure of this type of RNN is simple and the process for solving the optimization problem of LMI is parallel distributed, the physical feasibility test can be completed more quickly than when utilizing commonly used semi-definite programming techniques. By taking advantage of highly efficient computation capabilities, the proposed physical feasibility test algorithm is particularly suitable for identification approaches that require rapid feasibility assessment such as on-line identification methods or optimization-based identification methods. An evolutionary algorithm-based identification method is used as an illustrative example to assess the performance of the proposed approach. In addition, a stability proof for the proposed gradient-type RNN is also provided. Results of the experiments conducted on a 6-DOF industrial robot manipulator verify the effectiveness of the proposed approach.INDEX TERMS Dynamic parameter identification, industrial robot manipulator, physical feasibility, recurrent neural network.

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“…= L (3) xy − a (4) l (4) z − 6 i=4 a (4) d ( 4) M (i) , β (11) = L (3) xz , β (12) = L (3) yz , β (13) = L (3) zz + L (4) yy + 2d (4)…”

Section: Appendix a Symbolic Expressions Of The Base Parameters Of Th...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Rapid Base Parameter Physical Feasibility Test Algorithm for Industrial Robot Manipulator Identification Using a Recurrent Neural Network

Huang,

Cheng,

Huang

2023

IEEE Access

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show abstract

“…Although linear models are attractive for many reasons, they have their limitations, especially since all physical systems are nonlinear to some extent, and in many cases linear models are not suitable for representing these systems. In this regard, there is currently a significant interest in methods for identifying nonlinear systems, especially using machine learning methods based on neural networks [16][17][18].…”

Section: Problem Statement Of the Control Object Identification By An...mentioning

confidence: 99%

Autonomous Navigation of Mobile Robot Assisted by Its Identified Neural Network Model

Prokopiev

Shmalko

Diveev

2023

15th International Conference &Amp;ldquo;Intelligent Systems” (INTELS’22)

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Parametric Identification of the Mathematical Model of a Mobile Robot with Mecanum Wheels

Hendzel

Kołodziej

2023

Lecture Notes in Networks and Systems

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Parameter Identification of a Robot Arm Manipulator Based on a Convolutional Neural Network

Cited by 4 publications

References 29 publications

A Rapid Base Parameter Physical Feasibility Test Algorithm for Industrial Robot Manipulator Identification Using a Recurrent Neural Network

A Rapid Base Parameter Physical Feasibility Test Algorithm for Industrial Robot Manipulator Identification Using a Recurrent Neural Network

Autonomous Navigation of Mobile Robot Assisted by Its Identified Neural Network Model

Parametric Identification of the Mathematical Model of a Mobile Robot with Mecanum Wheels

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